Evaluation of Various Offline and Online ECM Parameter Identification Methods of Lithium-Ion Batteries in Underwater Vehicles

Abstract

For underwater vehicles, the state of charge (SOC) of battery is often used to guide the optimal allocation of energy. An accurate SOC estimation can improve work efficiency and reliability of underwater vehicles. Model-based SOC estimation methods are still mainstream routes used in practical applications. Hence, accurate battery models are highly desirable, which depends not only on the circuit structure but also on the circuit parameters. Four-parameter identification algorithms, offline mechanism-based and least squared (LS) methods, as well as online recursive least-squares with forget factor (FFRLS) and extended Kalman filter (EKF) methods were analyzed in terms of SOC estimation under three different conditions. The results revealed that in the case without any disturbance, the predicted SOCs based on four-parameter identification circuits fitted well with the reference. Moreover, it is remarkable that the LS offline methods work better than the FFRLS online routes. In addition, the robustness has also been accessed through the other two conditions, i.e., measurement data with disturbance and initial SOC value with deviation. The results showed that maximum errors of SOC estimation based on the EKF approach are significantly lower than those of the other methods, and the values are 0.51% and 0.20%, respectively. Thus, the circuit model based on the EKF parameter identification approach possessed a stronger anti-interference performance during the SOC estimation process. This research can provide corresponding theoretical support on ECM parameter identification for lithium-ion batteries in underwater vehicles.

Review—Optimized Particle Filtering Strategies for High-Accuracy State of Charge Estimation of LIBs

“…96 It is widely used because it clearly shows the mathematical relationship between each element of the LIB, its current-voltage characteristics, and its SOC value. 97 The EECM uses electrical components such as resistors, capacitors, voltage sources, etc, to describe the charging and discharging characteristics of a LIB through different combinations, which is a semi-empirical model. 98 Generally, an ECM consists of three components, which include the open-circuit voltage (OCV) source, the ohmic resistor, 99 and the resistor-capacitor (RC) circuit networks.…”

“…121 Offline parameter identification usually requires a large amount of experimental data and computing resources, so careful planning and execution of experiments and efficient algorithms for data processing and model fitting are needed. 122 This identification method relies on testing experiments. Taking the simplest first-order equivalent circuit as an example, model parameters are obtained through HPPC testing experiments.…”

Anti-disturbance State-of-Charge Estimation for Lithium-ion Batteries Using Nonlinear Extended State Observers

“…where Uoc denotes the OCV of the battery and g(• ) represents the nonlinear connection between the SOC and OCV. In addition, the Usoc(t) and SOC(t) are interchangeable in the battery model [28]. Rself denotes the self-charging resistor.…”

Online parameter identification for equivalent circuit model of lithium-ion battery

“…These models simulate battery reactions and inform charging and discharging strategies. Besides the electrochemical model [3]- [5], and the black box model [6], the equivalent circuit model (ECM) [7]- [10] is widely used due to its simplicity of battery simulation [11]. In the ECM, the voltage response of a battery can be well modeled on different time scales by connecting RC cells with different time constants in series [12], [13].…”